Input Design for Online Fault Diagnosis of Nonlinear Systems with Stochastic Uncertainty

Fault diagnosis is crucial for ensuring stable and reliable operation of high-performance systems in the presence of abnormal events. System uncertainties often make discrimination between normal and faulty behavior a challenging task. This paper presents an active fault diagnosis (AFD) method for nonlinear systems with stochastic uncertainty. AFD involves the optimal design of system inputs for discriminating between multiple model hypotheses that correspond to various operational scenarios. The proposed AFD method relies on minimizing the probability of error in hypothesis selection subject to hard input and state chance constraints. Moment-based approximations for a bound on the probability of error in hypothesis selection as well as for chance constraint evaluation are introduced in order to derive a tractable surrogate AFD problem that is amenable to online implementations. The performance of the AFD method for offline and online fault diagnosis is demonstrated on a continuous bioreactor with multiple operational scenarios. Simulation results demonstrate the effectiveness of the proposed method for online AFD in a practical setting.